This course is a graduate version of 03-363. Students will attend the same lectures as the students in 03-363, plus an additional once weekly meeting. In this meeting, topics covered in the lectures will be addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature as well as current papers that illustrate cutting edge approaches to systems neuroscience or important new concepts. Use of animals as research model systems will also be discussed. Performance in this portion of the class will be assessed by supplemental exam questions as well as by additional homework assignments.

Prerequisites: 03121 AND (03362 or 03762)

03-815 Magnetic Resonance Imaging in Neuroscience: 12 Units

Instructor: Eric Ahrens

Location: Doherty Hall 1209

Days/Times: T/R 10:30AM – 11:50AM

The course is designed to introduce students to the fundamental principles of magnetic resonance imaging (MRI) and its application in neuroscience. MRI is emerging as the preeminent method to obtain structural and functional information about the living human brain. This methodology has helped to revolutionize neuroscience and the study of human cognition. The specific topics covered in this course will include: introduction to spin gymnastics, survey of imaging methods, structural brain mapping, functional MRI (fMRI), and MR spectroscopy (MRS). Approximately, one third of the course will be devoted to introductory concepts of magnetic resonance, another third to the discussion of MRI methods, and the remaining third will cover a broad range of neuroscience applications. Guest lectures will be incorporated into the course from neuroscientists and psychologists who use MRI in their own research.

CMU CNBC

86-631 Neural Data Analysis: 9 units
(Cross listed as 42-631)

Instructor: Steve Chase

Date/Time: T/R 1:30 PM – 2:50 PM

Location: MI 130

The vast majority of behaviorally relevant information is transmitted through the brain by neurons as trains of actions potentials. How can we understand the information being transmitted? This class will cover the basic engineering and statistical tools in common use for analyzing neural spike train data, with an emphasis on hands-on application. Topics may include neural spike train statistics (Poisson processes, interspike intervals, Fano factor analysis), estimation (MLE, MAP), signal detection theory (d-prime, ROC analysis, psychometric curve fitting), information theory, discrete classification, continuous decoding (PVA, OLE), and white-noise analysis. Each topic covered will be linked back to the central ideas from undergraduate probability, and each assignment will involve actual analysis of neural data, either real or simulated, using Matlab. This class is meant for upper-level undergrads or beginning graduate students, and is geared to the engineer who wants to learn the neurophysiologist’s toolbox and the neurophysiologist who wants to learn new tools. Those looking for broader neuroscience application (eg, fMRI) or more focus on regression analysis are encouraged to take 36-746. Those looking for more advanced techniques are encouraged to take 18-699. Prerequisites: undergraduate probability (36-225/227, or its equivalent), some familiarity with linear algebra and Matlab programming

86-675 Computational Perception : 12 units

Instructor: Tai Sing Lee

Date/Time: M/W/F 3:30 PM – 4:20 PM

Location: MI 130

In this course, we will first cover the biological and psychological foundational knowledge of biological perceptual systems, and then apply computational thinking to investigate the principles and mechanisms underlying natural perception. The course will focus on vision this year, but will also touch upon other sensory modalities. You will learn how to reason scientifically and computationally about problems and issues in perception, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. Topics include perceptual representation and inference, perceptual organization, perceptual constancy, object recognition, learning and scene analysis. Prerequisites: First year college calculus, some basic knowledge of linear algebra and probability and some programming experience are desirable.

CMU Computer Science

15-781 Machine Learning: 12 Units

Instructor: Geoffrey Gordon & Alexander Smola

Location: Wean Hall 7500

Days/Times: M/W 10:30AM – 11:50AM

Machine learning studies the question “How can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory.

Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. A detailed curriculum from an earlier semester is available at http://www.cs.cmu.edu/%7Etom/10701_sp11/lectures.shtml

This course is an in-depth study of information processing in real neural systems from a computer science perspective. We will examine several brain areas, such as the hippocampus and cerebellum, where processing is sufficiently well understood that it can be discussed in terms of specific representations and algorithms. We will focus primarily on computer models of these systems, after establishing the necessary anatomical, physiological, and psychophysical context. There will be some neuroscience tutorial lectures for those with no prior background in this area.

Machine learning studies the question “How can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from from machine learning, from classical statistics, from data mining, from Bayesian statistics and from information theory.

Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. A detailed curriculum from an earlier semester is available at http://www.cs.cmu.edu/%7Etom/10701_sp11/lectures.shtml

CMU Psychology

85-708 Visual Cognition: 12 units

Instructor: David Plaut

Location: Baker Hall 336B

Days/Times: T/R 1:30PM – 2:50PM

Recognizing an object, face or word is a complex process which is mastered with little effort by humans. This course adopts a three-pronged approach, drawing on psychological, neural and computational models to explore a range of topics including early vision, visual attention, face recognition, reading, object recognition, and visual imagery. The course will take a seminar format.

85-723 Cognitve Development: 12 units

Instructor: Robert Siegler

Location: Baker Hall 342E

Days/Times: T/R 1:30PM – 2:50PM

The general goals of this course are that students become familiar with the basic phenomena and the leading theories of cognitive development, and that they learn to critically evaluate research in the area. Piagetian and information processing approaches will be discussed and contrasted. The focus will be upon the development of childrens information processing capacity and the effect that differences in capacities have upon the childs ability to interact with the environment in problem solving and learning situations.

This course will cover fundamental findings and approaches in cognitive neuroscience, with the goal of providing an overview of the field at an advanced level. Topics will include high-level vision, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, i.e. computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans and single-neuron recording in animals. Lecture format will be used for most sessions, with a few sessions devoted to discussion.

Special permission is required: Graduate Students, instructors permission from Carl Olson at colson@cnbc.cmu.edu and once you have instructor’s permission, please see Erin Donahoe , in BH 342 E or donahoe@andrew.cmu.edu to register you.

85-770 Perception: 12 units

Instructor: Roberta Klatzky

Location: Baker Hall 336B

Days/Times: T/R 9:00AM – 10:20AM

Perception, broadly defined, is the construction of a representation of the external world for purposes of thinking and acting. Although we often think of perception as the processing of inputs to the sense organs, the world conveyed by the senses is ambiguous, and cognitive and sensory systems interact to interpret it. In this course, we will examine the sensory-level mechanisms involved in perception by various sensory modalities, including vision, audition, and touch. We will learn how sensory coding interacts with top-down processing based on context and prior knowledge and how perception changes with learning and development. We will look at methods of psychophysics, neuroscience, and cognitive psychology. The goals include not only imparting basic knowledge about perception but also providing new insights into everyday experiences.

85-790 Human Memory: 9 units

Instructor: Lynne Reder

Location: Baker Hall 235B

Days/Times: M/W 1:30PM – 2:50PM

Without memory, people would barely be able to function: we could not communicate because we would not remember meanings of words, nor what anyone said to us; we could have no friends because everyone would be a stranger (no memory of meeting anyone); we could have no sense of self because we could not remember anything about ourselves either; we could not predict anything about the future because we would have no recollections of the past; we would not know how to get around, because we would have no knowledge of the environment. This course will discuss issues related to memory at all levels: the sensory registers, i.e., how we perceive things; working and short-term memory; long-term memory or our knowledge base. We will discuss recent advances in cognitive neuroscience as they inform our understanding of how human memory works. We will discuss the differences between procedural/skill knowledge, and declarative/fact knowledge and between implicit (memories that affect behavior without conscious awareness) and explicit memory (intentional or conscious recollections). Other topics will include clinical cases of memory problems such as various forms of amnesia.

85-806 Autism: Psychological and Neuroscience Perspectives: 12 units

Instructor: Marcel Just

Location: Baker Hall 336B

Days/Times: T 7:00PM – 9:50PM

Autism is a disorder that affects many cognitive and social processes, sparing some facets of thought while strongly impacting others. This seminar will examine the scientific research that has illuminated the nature of autism, focusing on its cognitive and biological aspects. For example, language, perception, and theory of mind are affected in autism. The readings will include a few short books and many primary journal articles. The readings will deal primarily with autism in people whose IQ?s are in the normal range (high functioning autism). Seminar members will be expected to regularly enter to class discussions and make presentations based on the readings. The seminar will examine various domains of thinking and various biological underpinnings of brain function, to converge on the most recent scientific consensus on the biological and psychological characterization of autism. There will be a special focus on brain imaging studies of autism, including both structural (MRI) imaging of brain morphology and functional (fMRI and PET) imaging of brain activation during the performance of various tasks.

CMU Robotics

16-720 Computer Vision: 12 units

Instructor: Martial Herbert

Location: Scaife Hall 125

Days/Times: M/W 12:00PM – 1:20PM

This course introduces the fundamental techniques used in computer vision, that is, the analysis of patterns in visual images to reconstruct and understand the objects and scenes that generated them. Topics covered include image formation and representation, camera geometry and calibration, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, image segmentation, object recognition. The material is based on graduate-level texts augmented with research papers, as appropriate. Evaluation is based on homeworks and final project. The homeworks involve considerable Matlab programming exercises.

This course examines advanced topics in emotion from a psychological perspective. Emotions are thought to relate to numerous response channels including physiology, neurobiology and expression (facial and vocal), and each of these components and their relationships will be examined. Class will center around discussion of primary sources. This is a joint undergraduate/graduate course, students will be enrolled only with the permission of the instructor.

CMU Statistics

36-707 Regression Analysis: 12 units

Instructor: TBA

Location: Porter Hall 226A

Days/Times: T/R 12:00PM – 1:20PM

This is a course in data analysis using mutiple linear regression. Topics covered include simple linear regression, ordinary least squares and weighted least squares, the geometry of least squares, quadratic forms, F tests and ANOVA tables, residuals, outlier detection, and identification of influential observations, variable selection methods, and modern regression techniques. Essential background in linear algebra is reviewed where necessary. When time permits other topics such as nonlinear regression and robust estimation will be discussed. Practice in data analysis is obtained through course projects.

36-749 Experimental Design for Behavioral and Social Sciences: 12 units

Statistical aspects of the design and analysis of planned experiments are studied in this course. A clear statement of the experimental factors will be emphasized. The design aspect will concentrate on choice of models, sample size and order of experimentation. The analysis phase will cover data collection and computation, especially analysis of variance, and will stress the interpretation of results. In addition to weekly lecture, students will attend a computer lab once a week. Prerequisite: 36-202, 36-220, or 36-247

Pitt Bioengineering

BIOENG 2650 Learning & Control and Movement CR HRS: 3.0

Instructor: Gelsy Torres

Location: Benedum Hall 320

Days/Times: M/W 3:00PM – 4:15PM

In this course we will discuss current theories on how the human motor system controls and learns movement. These theories are developed blending concepts from neuroscience, probability, and biomechanics . While motor control will be discussed as a feedback control problem, these theories will be compared to what we know about the motor system.
We will begin by studying muscle activation and forces, muscle sensory organs, spinal control structures, and inertial dynamics of a multi-joint limb. This will give us a sense of the machinery that the nervous system must control in order to generate coordinated movements. Probability foundations will be used as a framework to model how the nervous system updates estimates of limb position and sensory feedback during movements. Finally we will consider how disease can inform us about principles of movement control and motor learning.

Pitt Epidemiology

EPIDEM 2012 Principles of Neuroepidemiology CR HRS: 2.0

Instructor: Caterina Rosano

Location: TBA

Days/Times: R 5:00PM – 6:50PM

The Course in Neuroepidemiology focuses on the application of the methods of epidemiology to the problems of clinical neurology. This course covers epidemiological approaches, etiological perspectives and methodologies to assess disorders of the central nervous system (CNS), including cutting-edge neuroimaging methods. This course also provides guided and critical knowledge of existing neuroepidemiological studies through the research practicum. In addition to students pursuing Doctoral and Master level degrees in Epidemiology, this course is designed to reach trainees in a variety of fields, including medicine, neurology, psychiatry, physical medicine and rehabilitation, neuroscience, psychology and computer science.

Pitt Mathematics

MATH 3375 Computational Neuroscience CR HRS: 3.0 [CNBC core course]

Instructor: Brent Doiron

Location: Thackeray 525

Days/Times: M/W 9:30AM – 10:45AM

This course will present the fundamentals of neural modeling, with a focus on establishing the computations performed by single neurons and networks of neurons. The aim of the course is to provide students with the necessary knowledge and toolbox from which to simulate neural dynamics within the context of a processing task. Topics to be covered include Hodgkin-Huxley model of a neuron, dendritic integration, reduced neuron models, modeling synaptic dynamics, behavior of small networks of neurons, Weiner analysis of a spike train, spike train statistics, information theory applied to neural ensembles.

This course will cover fundamental findings and approaches in cognitive neuroscience, with the goal of providing an overview of the field at an advanced level. Topics will include high-level vision, spatial cognition, working memory, long-term memory, learning, language, executive control, and emotion. Each topic will be approached from a variety of methodological directions, i.e. computational modeling, cognitive assessment in brain-damaged humans, non-invasive brain monitoring in humans and single-neuron recording in animals. Lecture format will be used for most sessions, with a few sessions devoted to discussion.

Note: CNBC students must take both 2100 and 2101; the two parts are taught sequentially.

2100- This course is the first component of the introductory graduate sequence designed to provide an overview of cellular and molecular aspects of neuroscience. This course covers nerve cell biology, protein chemistry, regulation of gene expression, receptor function, and second messenger signaling in a lecture format. A conference designed to develop critical reading skills will cover primary literature corresponding to material covered in each block. Students will be expected to read and discuss original scientific literature.

2101- This course is the second component of the introductory graduate sequence designed to provide an overview of cellular and molecular aspects of neuroscience. This course covers the electrical properties of neurons, synaptic transmission and neural development.

Prerequisites: A background in basic biology and permission of the instructor is required.

Note for CMU students: Section 2 ofthe PCHE Cross Registration Request Form provides a space for students to enroll in a primary choice (course), and a secondary choice in case the primary is not available. Please register for the NROSCI sections as your primary chioce and the MSNBIO sections as your secondary choice, so that when NROSCI fills up, the Registrar’s Office will automatically put you in the MSNBIO section without having to complete any additional paperwork.

Note for non-Neuroscience students:The 2100/2101 sequence assumes a substantial background in biology. Students who lack this background and cannot devote substantial time to background reading might prefer to take Advanced Cellular Neuroscience instead.

‘Biology of Vision’ (INTBP2100) will introduce students to the basic biology of vision and vision-related research. Topics include: ocular anatomy and development; structure and function of the anterior segment; immunology and diseases of the eye; retinal structure, function and disease; imaging the visual system; visual perception; eye movements. The overall goal is to give students an understanding of the full range of vision research from a variety of methodological perspectives. The course is designed primarily for graduate students and post-doctoral fellows at Pitt and CMU, but interested advanced undergraduate students may contact one of the course coordinators for permission to register.

Pitt Psychology

PSY 2005 Statistical Analysis I / Advanced Statistics-UG: CR HRS: 3.0

Instructor: TBA

Location: Old Engineering Hall 303

Days/Times: M 2:00PM – 2:55PM

This course is the first of a two course sequence to provide the knowledge and skills needed to plan and conduct analyses using a uniform framework based on the general linear model. Students will learn techniques to conduct a variety of statistical tests; the appropriate interpretation of results will be emphasized. Topics include descriptive statistics, graphing data, sampling distributions, hypothesis testing (including power, effect sizes, and confidence intervals), T-tests, correlations, multiple regression, and polynomial regression. Students use SAS for statistical computations.